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Most statistical developments in the joint modelling area have focused on the shared random-effect models that include characteristics of the longitudinal marker as predictors in the model for the time-to-event. A less well-known approach is the joint latent class model which consists in assuming that a latent class structure entirely captures the correlation between the longitudinal marker trajectory and the risk of the event. Owing to its flexibility in modelling the dependency between the longitudinal marker and the event time, as well as its ability to include covariates, the joint latent class model may be particularly suited for prediction problems. This article aims at giving an overview of joint latent class modelling, especially in the prediction context. The authors introduce the model, discuss estimation and goodness-of-fit, and compare it with the shared random-effect model. Then, dynamic predictive tools derived from joint latent class models, as well as measures to evaluate their dynamic predictive accuracy, are presented. A detailed illustration of the methods is given in the context of the prediction of prostate cancer recurrence after radiation therapy based on repeated measures of Prostate Specific Antigen.

Purpose To quantify the influence of RS assay on changing chemotherapy plans in a general practice setting using causal inference methods. Methods We surveyed 3880 newly diagnosed breast cancer patients in Los Angeles and Georgia in 2013–14. We used inverse propensity weighting and multiple imputations to derive complete information for each patient about treatment status with and without testing. Results A half of the 1545 women eligible for testing (ER+ or PR+, HER2−, and stage I–II) received RS. We estimate that 30% (95% confidence interval (CI) 10–49%) of patients would have changed their treatment selections after RS assay, with 10% (CI 0–20%) being encouraged to undergo chemotherapy and 20% (CI 10–30%) being discouraged from chemotherapy. The subgroups whose treatment selections would be changed the most by RS were patients with positive nodes (44%; CI 24–64%), larger tumor (43% for tumor size >2 cm; CI 23–62%), or younger age (41% for <50 years, CI 23–58%). The assay was associated with a net reduction in chemotherapy use by 10% (CI 4–16%). The reduction was much greater for women with positive nodes (31%; CI 21–41%), larger tumor (30% for tumor size >2 cm; CI 22–38%), or younger age (22% for <50 years; CI 9–35%). Conclusion RS substantially changed chemotherapy treatment selections with the largest influence among patients with less favorable pre-test prognosis. Whether this is optimal awaits the results of clinical trials addressing the utility of RS testing in selected subgroups.

Patients who were previously treated for prostate cancer with radiation therapy are monitored at regular intervals using a laboratory test called Prostate Specific Antigen (PSA). If the value of the PSA test starts to rise, this is an indication that the prostate cancer is more likely to recur, and the patient may wish to initiate new treatments. Such patients could be helped in making medical decisions by an accurate estimate of the probability of recurrence of the cancer in the next few years. In this article, we describe the methodology for giving the probability of recurrence for a new patient, as implemented on a web‐based calculator. The methods use a joint longitudinal survival model. The model is developed on a training dataset of 2386 patients and tested on a dataset of 846 patients. Bayesian estimation methods are used with one Markov chain Monte Carlo (MCMC) algorithm developed for estimation of the parameters from the training dataset and a second quick MCMC developed for prediction of the risk of recurrence that uses the longitudinal PSA measures from a new patient.

High-throughput ‘omics’ technologies that generate molecular profiles for biospecimens have been extensively used in preclinical studies to reveal molecular subtypes and elucidate the biological mechanisms of disease, and in retrospective studies on clinical specimens to develop mathematical models to predict clinical endpoints. Nevertheless, the translation of these technologies into clinical tests that are useful for guiding management decisions for patients has been relatively slow. It can be difficult to determine when the body of evidence for an omics-based test is sufficiently comprehensive and reliable to support claims that it is ready for clinical use, or even that it is ready for definitive evaluation in a clinical trial in which it may be used to direct patient therapy. Reasons for this difficulty include the exploratory and retrospective nature of many of these studies, the complexity of these assays and their application to clinical specimens, and the many potential pitfalls inherent in the development of mathematical predictor models from the very high-dimensional data generated by these omics technologies. Here we present a checklist of criteria to consider when evaluating the body of evidence supporting the clinical use of a predictor to guide patient therapy. Included are issues pertaining to specimen and assay requirements, the soundness of the process for developing predictor models, expectations regarding clinical study design and conduct, and attention to regulatory, ethical, and legal issues. The proposed checklist should serve as a useful guide to investigators preparing proposals for studies involving the use of omics-based tests. The US National Cancer Institute plans to refer to these guidelines for review of proposals for studies involving omics tests, and it is hoped that other sponsors will adopt the checklist as well.

Clinical trials often collect intermediate or surrogate endpoints other than their true endpoint of interest. It is important that the treatment effect on the surrogate endpoint accurately predicts the treatment effect on the true endpoint. There are settings in which the proposed surrogate endpoint is positively correlated with the true endpoint, but the treatment has opposite effects on the surrogate and true endpoints, a phenomenon labeled “surrogate paradox”. Covariate information may be useful in predicting an individual’s risk of surrogate paradox. In this work, we propose methods for incorporating covariates into measures of assessing the risk of surrogate paradox using the meta-analytic causal association framework. The measures calculate the probability that a treatment will have opposite effects on the surrogate and true endpoints and determine the size of a positive treatment effect on the surrogate endpoint that would reduce the risk of a negative treatment effect on the true endpoint as a function of covariates, allowing the effects of covariates on the surrogate and true endpoint to vary across trials.

The hazard has been a central concept in the analysis and interpretation of time-to-event data for over 50 years. At any follow-up time, the hazard is the probability of the event happening in the next unit of time amongst those still at risk. Hazard ratios (HRs) between groups are frequently used to quantify the exposure/treatment’s association with the failure time. In a highly cited paper, Hernán criticized HRs, asserting that their decreases over time may reflect simply a built-in selection bias, induced by an unmeasured susceptibility, and should not be interpreted as genuine changes in treatment effect. Hernán supports his arguments mainly by the results of a hormone therapy trial, where the HR for coronary heart events decreased largely during follow-up, with hazards crossing from harmful to protective treatment effect. However, he did not present simulations or algebraic derivations to demonstrate that these changes may reflect just an unmeasured susceptibility. We use simulations and real-world case studies to systematically explore this issue. The first simulation series reveals how the underestimation bias and changes over time in Cox proportional hazards model-based HRs depend on the joint impact of susceptibility on the hazard, its distribution, and the incidence of events; with important bias toward the null occurring only for a very strong susceptibility. Further simulations mimic the hormone therapy trial highlighted by Hernán, to demonstrate that the reported bias and crossing hazards are extremely unlikely to reflect just an unmeasured susceptibility, which suggests some biological reasons for decaying treatment HR, possibly including decreasing treatment adherence. Finally, we present real-world examples of interpretable and clinically plausible time-dependent HRs in cancer research. In conclusion, results of our simulations and real-world case studies suggest that concerns about HR limitations may be overstated, and we encourage researchers to model time-dependent HRs and consider potential biological and clinical causes thereof.

Summary Background : AT-101 is a BCL-2 Homolog domain 3 mimetic previously demonstrated to have tumoricidal effects in advanced solid organ malignancies. Given the evidence of activity in xenograft models, treatment with AT-101 in combination with docetaxel is a therapeutic doublet of interest in metastatic head and neck squamous cell carcinoma. Patients and Methods : Patients included in this trial had unresectable, recurrent, or distantly metastatic head and neck squamous cell carcinoma (R/M HNSCC) not amenable to curative radiation or surgery. This was an open label randomized, phase II trial in which patients were administered AT-101 in addition to docetaxel. The three treatment arms were docetaxel, docetaxel plus pulse dose AT-101, and docetaxel plus metronomic dose AT-101. The primary endpoint of this trial was overall response rate. Results : Thirty-five patients were registered and 32 were evaluable for treatment response. Doublet therapy with AT-101 and docetaxel was well tolerated with only 2 patients discontinuing therapy due to treatment related toxicities. The overall response rate was 11 % (4 partial responses) with a clinical benefit rate of 74 %. Median progression free survival was 4.3 months (range: 0.7–13.7) and overall survival was 5.5 months (range: 0.4–24). No significant differences were noted between dosing strategies. Conclusion : Although met with a favorable toxicity profile, the addition of AT-101 to docetaxel in R/M HNSCC does not appear to demonstrate evidence of efficacy.

Patients treated for prostate cancer are monitored by periodically measuring prostate-specific antigen (PSA) after treatment. Increases in PSA are suggestive of cancer recurrence and are used in making decisions about possible new treatments. The data from studies of such patients typically consist of longitudinal PSA measurements, censored event times, and baseline covariates. Methods for the combined analysis of both longitudinal and survival data have been developed in recent years, with the main emphasis on modeling and estimation. We analyze data from a prostate cancer study in which the patients are treated with radiation therapy, using a joint model extended by adding a mixture structure to the model. Here we focus on using this model to make individualized predictions of disease progression for censored and alive patients. In this model, each patient is assumed to be either cured by the treatment or susceptible to clinical recurrence. The cured fraction is modeled as a logistic function of baseline covariates, measured before the end of the radiation therapy period. The longitudinal PSA data is modeled as a nonlinear hierarchical mixed model, with different models for the cured and susceptible groups. To accommodate the heavy tail manifested by the data and possible outliers, a t distribution is used for the measurement error. The clinical recurrences are modeled using a time-dependent proportional hazards model for those in the susceptible group, where the time-dependent covariates include both the current value and the slope the of posttreatment PSA profile. The baseline hazard is assumed to have a generalized Weibull form. Estimates of the parameters in the model are obtained using a Markov chain Monte Carlo method. The model is used to give individual predictions of both future PSA values and the predicted probability of recurrence up to four years in the future. These predictions are compared with observed data from a validation data set consisting of further follow-up of the subjects in the study. There is good correspondence between the predictions and the validation data.

PurposeSurveillance, Epidemiology, and End Results (SEER) cancer registries provides information about survival duration and cause of death for cancer patients. Baseline demographic and tumor characteristics such as age, sex, race, year of diagnosis, and tumor stage can inform the expected survival time of patients, but their associations with survival may not be constant over the post-diagnosis period.MethodsUsing SEER data, we examined if there were time-varying associations of patient and tumor characteristics on survival, and we assessed how these relationships differed across 14 cancer sites. Standard Cox proportional hazards models were extended to allow for time-varying associations and incorporated into a competing-risks framework, separately modeling cancer-specific and other-cause deaths. For each cancer site and for each of the five factors, we estimated the relative hazard ratio and absolute hazard over time in the presence of competing risks.ResultsOur comprehensive consideration of patient and tumor characteristics when estimating time-varying hazards showed that the associations of age, tumor stage at diagnosis, and race/ethnicity with risk of death (cancer-specific and other-cause) change over time for many cancers; characteristics of sex and year of diagnosis exhibit some time-varying patterns as well. Stage at diagnosis had the largest associations with survival.ConclusionThese findings suggest that proportional hazards assumptions are often violated when examining patient characteristics on cancer survival post-diagnosis. We discuss several interesting results where the relative hazards are time-varying and suggest possible interpretations. Based on the time-varying associations of several important covariates on survival after cancer diagnosis using a pan-cancer approach, the likelihood of the proportional hazards assumption being met or corresponding interpretation should be considered in survival analyses, as flawed inference may have implications for cancer care and policy.

Head and neck squamous cell carcinoma (HNSCC) is the eighth most commonly diagnosed cancer in the United States. The risk of developing HNSCC increases with exposure to tobacco, alcohol and infection with human papilloma virus (HPV). HPV-associated HNSCCs have a distinct risk profile and improved prognosis compared to cancers associated with tobacco and alcohol exposure. Epigenetic changes are an important mechanism in carcinogenic progression, but how these changes differ between viral- and chemical-induced cancers remains unknown. CpG methylation at 1505 CpG sites across 807 genes in 68 well-annotated HNSCC tumor samples from the University of Michigan Head and Neck SPORE patient population were quantified using the Illumina Goldengate Methylation Cancer Panel. Unsupervised hierarchical clustering based on methylation identified 6 distinct tumor clusters, which significantly differed by age, HPV status, and three year survival. Weighted linear modeling was used to identify differentially methylated genes based on epidemiological characteristics. Consistent with previous in vitro findings by our group, methylation of sites in the CCNA1 promoter was found to be higher in HPV(+) tumors, which was validated in an additional sample set of 128 tumors. After adjusting for cancer site, stage, age, gender, alcohol consumption, and smoking status, HPV status was found to be a significant predictor for DNA methylation at an additional 11 genes, including CASP8 and SYBL1. These findings provide insight into the epigenetic regulation of viral vs. chemical carcinogenesis and could provide novel targets for development of individualized therapeutic and prevention regimens based on environmental exposures.